Software Engineer, cuDNN – Deep Learning Full-time

Do you love writing fast code and crafting software systems to solve complex problems? We are looking for hardworking software engineers to help design, build, and ship cuDNN: our GPU-accelerated library of primitives for deep neural networks. Intelligent machines powered by AI computers that can learn, reason, and interact with people are no longer science fiction. This is truly an extraordinary time. The era of AI has begun, and we are powering it. If this role seems like a good match for your skills and interests, tell us why you think you might be a great fit for our team, and we’d love to tell you more about what we do!

What you’ll be doing:

• Develop production-quality software that ships as part of NVIDIA’s AI software stack, including optimized large language model (LLM) support.
• Analyze the performance of important workloads, tuning our current software, and proposing improvements for future software.
• Work with cross-collaborative teams of deep learning software engineers and GPU architects to innovate across applications like generative AI, autonomous driving, computer vision, and recommender systems.
• Adapt to the constantly evolving AI industry by being agile and excited to contribute across the codebase, including API design, software architecture, performance modeling, testing, and GPU kernel development.
• Mentoring junior engineers on the team.

What we need to see:

• M.S. degree in computer science (or similar) or equivalent experience.
• Strong programming skills in C/C++ development, work experience with CUDA development, and familiarity with Python.
• Good understanding of linear algebra.
• Familiarity with the latest trends in machine learning.
• Experience designing high level software architecture.
• Experience with performance analysis, profiling, and code optimization.
• Ability to work independently, define project goals and scope, and lead your own development effort.

Ways to stand out from the crowd:

• GPU programming and optimization expertise (e.g. CUDA or OpenCL).
• Practical experience with machine learning, especially deep learning.
• Experience with computer architecture and building performance models for CPUs, GPUs, or other accelerators.
• Excellent problem solving skills, including applications of algorithms and data structures.
• Strong experience with data science, statistical analysis, and visualization.